Research of Robust Algorithm for Understanding a Situation in a Dynamic Environment

1.针对日照变化的稳健目标检测算法和阴影的“平面投影立体视方法”有效地消除了运动目标投射的阴影区域。然而，这种方法有一个主要的问题，它不仅检测运动目标，而且还检测被一幅图像中的目标遮挡的道路区域。2.恶劣天气下的运动目标检测算法在能见度较差的天气中，如降雪或浓雾天气，能见度在短时间内变化很快，每个像素的强度变化很大。为了克服这些问题，该算法将输入图像划分为网格区域，并在每个区域中计算累积帧数不同的两个直方图之间的互相关值。当移动对象进入该区域时，通过累积几个帧而生成的短累积直方图快速变化。另一方面，由于累积的帧数目越多而产生的长时间累积的直方图变化较慢。因此，通过测量短累积直方图和长累积直方图之间的相互关系的变化来检测运动目标。3.从图像序列中测量可见度的算法该算法将输入图像划分为网格区域，并通过测量每个区域的亮度变化来解决降雪或黑暗的程度。4.基于立体图像序列的姿态识别算法提出了一种扩展时间模板的手势识别方法，使其不仅可以包含从双目立体视觉获得的垂直和水平运动信息，还可以包含深度信息。

1.Robust object detection algorithm toward sunshine change and a shadow"Planar Projection Stereopsis Method" is effective to eliminate shadow areas casted by moving objects. However, this method has a major problem that it detects not only moving objects but also road areas occluded by objects in one image. In this research, this problem has been solved by conceiving new algorithm for eliminating occluded areas, comparing contours of areas detected by the planar projection stereopsis method with edges in an original image.2.Robust moving object detection algorithm in bad weatherIn the weather whose visibility is bad, such as in a snowfall or in a dense fog, the visibility changes rapidly in a short time and the intensity of each pixel changes hard every frame. In order to overcome these problems, the proposed algorithm divides an input image into grid regions and in each region, calculates a cross correlation between two histograms whose accumulated number of frames are different. A short accumulated histogram, generated from accumulating a few number of frames, changes quickly whenever moving objects go into the region. On the other hand, a long accumulated histogram, generated from accumulating the more number of frames, changes slowly. Therefore, moving objects are detected by measuring a variation on a cross correlation between a short accumulated histogram and a long accumulated histogram.3.Algorithm for measuring visibility from image sequencesThe proposed algorithm divides an input image into grid regions and solves the degree of the snowfall or darkness by measuring the change of intensity variance in each region.4.Gesture recognition algorithm using stereo image sequencesA gesture recognition method extending Temporal Templates so that they can contain not only vertical and horizontal motion but also depth information obtained from a binocular stereopsis has been conceived.